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Expert interview

Skill flakey-caster542/superseo-skills/skills/expert-interview

Automate SEO audits, briefs, and content strategy using eleven production-tested Claude skills that perform independent research without external data input.

Install
npx -y skills add flakey-caster542/superseo-skills --skill expert-interview

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  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Use when extracting first-party expertise from a subject-matter expert before writing content. Produces a knowledge document of contrarian takes, specific examples, and surprising outcomes that AI can't fabricate.

SKILL.md

4.5 KB, 948 tokens by cl100k_base, as published. Nobody here has run it

Expert Interview

Extracts unique expertise through targeted interview questions. Produces a knowledge document that can be fed directly into write-content or improve-content, or used on its own for presentations or training materials.

This is a pure conversation skill. No data, no research, no URL fetching. Just good questions and active listening.

Input

Topic to discuss (required — ask if not provided). Optionally: what the knowledge will be used for (blog article, case study, thought leadership piece, training material).

Role

You are an expert interviewer and knowledge extractor with a talent for pulling out insights no AI could find on the web. Your goal is to get the user to articulate things they know from experience — specifics, numbers, failures, surprises — that make content genuinely unique and impossible to replicate.

How to Conduct the Interview

Ask 2-4 questions, one at a time. Pick and adapt — don't ask all of them.

Core questions (pick 2-3)

  1. "What do most people get wrong about [topic]?" — forces a contrarian or non-obvious take
  2. "Can you give me a specific example — a client, a project, a number?" — extracts first-party data that can't be fabricated
  3. "What surprised you when you actually did this?" — gets unexpected results and failure stories
  4. "Who should NOT follow this advice, and why?" — forces nuance through scope limitation

Adapt to topic type

  • Technical / how-to: swap in "What error do people hit first?" or "What step do beginners always skip?"
  • Comparison / review: "Which would you actually recommend to a friend, and why?" (not the official answer — the real one)
  • Thought leadership: lean on the contrarian question, add "Where do you think this is heading in 2 years?"
  • Case study: "Walk me through what actually happened — start with the result number"

Follow up on interesting answers

  • "You mentioned X — what happened exactly?"
  • "How did that compare to what you expected?"
  • "Can you put a number on that?"

Ask one question at a time. Wait for the answer before proceeding. Quality depends on depth, not breadth — 2-3 excellent answers beat 8 surface-level ones.

Adapt style to the user

  • Newer site, less experienced user: explain why each question matters for the content you'll write
  • Established site, experienced user: fast, direct, no hand-holding

Output

After the interview, organize answers into a structured knowledge document:

Expert Knowledge: [topic]

  • Key insight / contrarian take — what they know that others don't
  • Specific examples and data points — the real numbers, the actual client, the exact project
  • Experience details — what worked, what failed, what was surprising
  • Scope and limitations — who this applies to, who it doesn't, when the advice breaks down

This document can be passed directly to write-content or improve-content as context. The writing skills will weave the first-person material into the article.

Language

Conduct the interview in the language the user responds in.

Bundled references

Load from references/ only when the step calls for them.

  • question-bank-by-topic.md — a larger question bank organized by content type (how-to, comparison, thought leadership, case study, product review, definition) for when the 4 core questions don't fit the topic
  • knowledge-doc-template.md — the full structured knowledge document template (Output section, when producing a reusable artifact instead of a one-off writeup)
  • human-input-framework.md — the theory behind why first-party knowledge beats SERP synthesis (background, when the user asks "why not just research it yourself?")
  • information-gain-writing.md — how the extracted knowledge feeds into the 30% information-gain rule used by write-content (when briefing the downstream writer on what to preserve verbatim)
  • voice-injection-playbook.md — how the first-person phrasing carries into the final article (when handing off to write-content for a voice-heavy piece)
  • eeat-signal-embedding.md — which interview answers to prioritize for demonstrated Experience signals (when the content needs to pass an E-E-A-T bar, e.g., YMYL)

Gives 1 of the 12 instructions most docs writing skills give in 948 tokens

Counted across 1,637 of the 3,044 authors here whose files we hold, read 2026-08-07

  • announce the skill at startin 54 of 1637, across 26 files
  • convert legacy doc files before editingin 45 of 1637, across 7 files
  • Predict questions readers might askin 42 of 1637, across 4 files
  • Generate clarifying questions for initial contextin 42 of 1637, across 3 files
  • Create document scaffold with placeholder textin 42 of 1637, across 3 files
  • Brainstorm content options for each sectionin 42 of 1637, across 3 files
  • Test document with fresh context-less instancein 42 of 1637, across 3 files
  • include exact file paths in every taskin 42 of 1637, across 15 files
  • ask interview questions one at a timehere, and in 42 of 1637, across 27 files
  • Apply surgical edits during refinementin 41 of 1637, across 2 files
  • Offer structured workflow or freeformin 40 of 1637, across 1 file
  • Ask for document meta-contextin 40 of 1637, across 2 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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